Amazon ECS service utilization metrics use cases

Resource utilization monitoring: Use average statistics to monitor CPU and memory consumption patterns, establish performance baselines, and detect gradual performance degradation trends.

Cost optimization: Use average statistics to monitor resource usage, right-size containers, adjust reservations based on usage patterns, and implement scheduled scaling.

Performance benchmarking: Use average statistics to compare metrics between service revisions, establish performance KPIs, and validate optimization improvements.

Resource floor detection: Use average statistics to identify minimum resource needs during idle periods, set appropriate reservations, and detect abnormal drops.

Anomaly detection: Use minimum statistics to spot unusual resource utilization drops that indicate potential problems, such as initialization failures or unexpected idle periods.

Scaling policy refinement: Use minimum statistics to establish optimal scale-in thresholds based on minimum viable utilization to prevent aggressive scaling.

Capacity planning: Use maximum statistics to set appropriate task sizes and plan infrastructure with sufficient headroom for traffic spikes.

Performance bottleneck identification: Use maximum statistics to detect resource saturation points, identify bottlenecks, and determine when to increase task size.

Scaling policy configuration: Use maximum statistics to set optimal scale-out thresholds based on peak patterns and configure burst capacity.

SLA compliance monitoring: Use maximum statistics to track peak response times and error rates to ensure service performance meets defined SLAs.